WO2008095031A1 - Inférence probabiliste de démographie de site à partir d'utilisations utilisateur internet agrégées et information démographique source - Google Patents

Inférence probabiliste de démographie de site à partir d'utilisations utilisateur internet agrégées et information démographique source Download PDF

Info

Publication number
WO2008095031A1
WO2008095031A1 PCT/US2008/052515 US2008052515W WO2008095031A1 WO 2008095031 A1 WO2008095031 A1 WO 2008095031A1 US 2008052515 W US2008052515 W US 2008052515W WO 2008095031 A1 WO2008095031 A1 WO 2008095031A1
Authority
WO
WIPO (PCT)
Prior art keywords
users
demographic attribute
attribute value
source
sink
Prior art date
Application number
PCT/US2008/052515
Other languages
English (en)
Inventor
Ching Law
Gokul Rajaram
Rama Ranganath
Original Assignee
Google, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Google, Inc. filed Critical Google, Inc.
Publication of WO2008095031A1 publication Critical patent/WO2008095031A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0204—Market segmentation

Definitions

  • the present invention concerns determining demographic information.
  • the present invention concerns probabilistically determining demographic information for a domain, such as a Website for example.
  • Demographic targeting is an important mode of targeting used by advertisers.
  • demographic information is typically only available for large Websites on the Internet. This is likely because the third parties that supply demographic information do so using a panel of 50,000 - 100,000 users. Consequently, these third parties can only get statistically significant user data for large Websites. This means that there is no way for these third parties to infer the user demographics for the vast majority of Websites on the Internet. This is unfortunate, because having reliable Internet-wide demographics, would enable more advertising revenue to become available to smaller Websites, instead of just the large ones for which demographics are known.
  • Website owners could self-describe their demographics. However, advertisers would probably not trust data supplied directly by the Website owner. For example, Website owners have an incentive to say "My visitors are all spendthrift millionaires", whether or not this is true, in order to attract high-revenue advertisements.
  • Embodiments consistent with the present invention may be used to determine a demographic attribute value of a sink online document given a set of users each of whom visited at least one of the source documents and the sink document. At least some of these embodiments may do so by (a) accepting a set of one or more values of the demographic attribute, each of the one or more demographic attribute values being associated with a source online document, wherein each of the source online documents has a value for the demographic attribute and has been visited by at least one user of the given set, (b) determining an estimate of the demographic attribute value of each of the users of the given set using the accepted demographic attribute value of each of the source online documents visited by the user, and (c) determining the demographic attribute value of the sink online document using the determined estimate of the demographic attribute value of each of the users of the given set.
  • the documents are Web pages, or Websites.
  • Figure l is a bubble diagram illustrating various operations that may be performed, and various information that may be used and/or generated, by exemplary embodiments consistent with the present invention.
  • Figure 2 is a flow diagram of an exemplary method for performing the general operations for estimating demographic information of a Website in a manner consistent with the present invention.
  • Figure 3 is a flow diagram of an exemplary method for estimating demographic information of a Website in a manner consistent with the present invention.
  • Figure 4 is a block diagram of an exemplary apparatus that may perform various operations, and store information used and/or generated by such operations, in a manner consistent with the present invention.
  • the present invention may involve novel methods, apparatus, message formats, and/or data structures for determining demographic information of a Website by using a set of source Websites with known demographic information and a given set of users each of whom visited at least one of the source Websites and the Website.
  • the following description is presented to enable one skilled in the art to make and use the invention, and is provided in the context of particular applications and their requirements.
  • the following description of embodiments consistent with the present invention provides illustration and description, but is not intended to be exhaustive or to limit the present invention to the precise form disclosed.
  • Various modifications to the disclosed embodiments will be apparent to those skilled in the art, and the general principles set forth below may be applied to other embodiments and applications.
  • FIG. 1 is a bubble diagram illustrating various operations that may be performed, and various information that may be used and/or generated, by exemplary embodiments consistent with the present invention.
  • demographic information of source online documents (seed websites) 110 may be available to the user demographic information estimation operation 150.
  • the 150 operations may obtain user information from user's 130 client device (e.g., browser toolbar). Such user information may be used to draw a given set of users, each of whom visited at least one of the source online documents and sink online documents.
  • Such user information may be generated by tracking users moving across various Websites (both source (seed) Websites 110 and sink (non-seed) Websites 120) with the help of browser toolbar.
  • the operations 150 may estimate user demographic information for all users in the given set of users defined above.
  • the estimated demographic information of each user in the given set generated by the operations 150 may be provided to the demographic information estimation operations
  • the operations 160 may use the estimated demographic information of each user within the given set to determine estimated demographic information 170 of sink online documents
  • FIG 2 is a flow diagram of an exemplary method 200 that might be used to probabilistically estimate demographic information of a domain or Website in a manner consistent with the present invention.
  • the method 200 may accept exact demographic information from a set of source online documents (e.g., seed Websites). (Block 210) Thereafter, the method 200 may probabilistically estimate demographic information of sink online documents (e.g., non-seed Websites) by using demographic information of source online documents and the pair-wise relationship between the documents (both sink and source online documents). (Block 220)
  • source online documents e.g., seed Websites
  • sink online documents e.g., non-seed Websites
  • the method 200 might probabilistically estimate demographic information as follows.
  • d be a demographics attribute, which is a function a set of Websites to a probability.
  • d(s) e [0,1] for any Website s .
  • d(s) is considered as the minimum probability that a pageview on Website s would satisfy this demographics attribute (i.e., that the pageview would be by a user with the demographic attribute).
  • d(site.com) 0.5 means that a pageview on site.com has a minimum probability of 0.5 of being generated from a visitor of age 25-34.
  • p be a function on set of edges of the graph G, where nodes of the graph G represent domains (e.g., Websites) or Web pages.
  • nodes of the graph G represent domains (e.g., Websites) or Web pages.
  • p(a,b) represent the probability that a pageview at Website b is initiated by a visitor of Website a .
  • Some embodiments consistent with the present invention might use a damping factor a e (0,1) to express how dependent or independent the traffic is of the demographics property. Specifically, if the traffic data is independent of the demographics property, then a would be 1 (1 means no damping factor at all, which is the case when the traffic data is independent of demographics). Otherwise a would be a factor less than 1 indicating some preservation of demographics property in the traffic flow.
  • a reasonable value for a can be derived by observing the demographics of source Websites for which there is traffic data. For example, if only users of a certain demographics property move from Website A to Website B, and if users without this property would move to Website C, then a might be set close to zero for this particular property.
  • a lower-bound estimate of the demographics d on t as contributed by s can be determined as follows: p(s,t)x d(s) x a
  • Websites are independent. Further, given the fact that u visits both Websites, they cannot be assumed to be totally independent.
  • all Websites visited by u may be expressed as S 11 - ⁇ u e S
  • v(u,s) l ⁇ .
  • the estimated value of the demographic for the user can be expressed as:
  • the users demographics approach can work with either pageviews or unique users.
  • the above formula estimates the demographics of a random visitor of Website t. If frequency estimates of the visitors of Website t are also available, then the demographics of a random pageview at the Website t can also be estimated.
  • the demographics of the Website s can be estimated with either of foregoing techniques.
  • the estimate may then be compared with the given (actual) value d(s) .
  • the estimates should not exceed the provided d ⁇ s) values for most of the Websites in S .
  • FIG 3 is a flow diagram of an exemplary method 300 that may be used to estimate demographic information of a document (referred to as a "Sink online document") such as a domain or Website for example, in a manner consistent with the present invention.
  • the method 300 may accept a set of one or more values of the demographic attribute, each of which being associated with a source online document, wherein each of the source online documents has a value for the demographic attribute and has been visited by at least one user of a set of users who have also visited a sink document. (Block 310)
  • the method 300 may determine an estimate of the demographic attribute value of each of the users of the given set using the accepted demographic attribute value of each of the source online documents visited by the user.
  • the method 300 may determine the demographic attribute value of the sink online document using the determined estimate of the demographic attribute value of each of the users of the given set. (Block 330)
  • the given set of users might be users who have visited both at least one of the source documents and the sink document. This set of users can be derived from browser toolbars which can track Websites visited by users.
  • the method 300 might determine an estimate of the demographic attribute value of each of the users in the given set by (i) summing, over all the source online documents visited by the user, the corresponding demographic attribute value of the source online documents to generate a summing result, and (ii) dividing the summing result with the number of source online documents visited by the user.
  • the method 300 may determine the demographic attribute value of the sink online document by (i) summing, over all the users of the given set, the corresponding determined estimate of the demographic attribute value of each of the users to generate a summing result, and (ii) dividing the summing result with the number of users of the given set.
  • FIG. 4 is high-level block diagram of a machine 400 that may perform one or more of the operations discussed above.
  • the machine 400 basically includes one or more processors 410, one or more input/output interface units 430, one or more storage devices 420, and one or more system buses and/or networks 440 for facilitating the communication of information among the coupled elements.
  • One or more input devices 432 and one or more output devices 434 may be coupled with the one or more input/output interfaces 430.
  • the machine 400 may be, for example, an advertising server, or it may be a plurality of servers distributed over a network.
  • the one or more processors 410 may execute machine-executable instructions (e.g., C or C++ running on the Solaris operating system available from Sun Microsystems Inc.
  • the machine 400 may be one or more conventional personal computers.
  • the processing units 410 may be one or more microprocessors.
  • the bus 440 may include a system bus.
  • the storage devices 420 may include system memory, such as read only memory (ROM) and/or random access memory (RAM).
  • the storage devices 420 may also include a hard disk drive for reading from and writing to a hard disk, a magnetic disk drive for reading from or writing to a (e.g., removable) magnetic disk, and an optical disk drive for reading from or writing to a removable (magneto-) optical disk such as a compact disk or other (magneto-) optical media.
  • ROM read only memory
  • RAM random access memory
  • the storage devices 420 may also include a hard disk drive for reading from and writing to a hard disk, a magnetic disk drive for reading from or writing to a (e.g., removable) magnetic disk, and an optical disk drive for reading from or writing to a removable (magneto-) optical disk such as a compact disk or other (magneto-) optical media.
  • a user may enter commands and information into the personal computer through input devices 432, such as a keyboard and pointing device (e.g., a mouse) for example.
  • Other input devices such as a microphone, a joystick, a game pad, a satellite dish, a scanner, or the like, may also (or alternatively) be included.
  • These and other input devices are often connected to the processing unit(s) 410 through an appropriate interface 430 coupled to the system bus 440.
  • the output devices 434 may include a monitor or other type of display device, which may also be connected to the system bus 440 via an appropriate interface.
  • the personal computer may include other (peripheral) output devices (not shown), such as speakers and printers for example.
  • the online documents might be documents served by server computers.
  • the users 130 might access the online documents using a client device, such as a personal computer, a mobile telephone, a mobile device, etc., having a browser.
  • the operations 150 and 160 might be performed by one or more computers.
  • the source demographic attribute information might be exact or non-exact demographic information of a small set of large Websites. This information might be collected from the Internet surfing behavior of opted-in panelists (e.g., 50,000 - 100,000 in number) whose exact demographics are known. For each Website in this list, the information supplied might include one or more of the following demographic information: Age, Gender, Household Income, Education, # Children (Household size), Connection speed, etc. Thus, this data might be used as "seed" data.
  • Websites are as described above. Further, it is assumed that the universe of all users is
  • the first step in this approach is to estimate the demographic property d(u) of user u .
  • Se S 11 e(u) [0045]
  • S u is the set of seed Websites visited by userw .
  • U ⁇ u l ,u 2 ,u 3 ,u 4 ,u 5 ⁇ are the following:
  • Website 5 are male and 78% of users visiting Website S 4 are male.
  • Websites S 1 and S 4 in a similar manner.
  • embodiments consistent with the present invention may be used to provide useful estimates of demographic information for domains, such as Websites for example.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Game Theory and Decision Science (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Economics (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Il est possible de déterminer une valeur d'attribut démographique pour un document collecteur en ligne sur la base d'un ensemble d'utilisateurs qui ont visité au moins un des documents sources et le site collecteur (a) en acceptant une ou des valeurs de l'attribut démographique (chacune associée à un document collecteur en ligne (où chacun des documents sources en ligne a une valeur pour l'attribut démographique et a été visité par au moins un utilisateur de l'ensemble susdonné), (b) en déterminant une estimation de la valeur d'attribut démographique pour chacun des utilisateurs de l'ensemble donné au moyen de la valeur d'attribut démographique acceptée de chacun des documents sources en ligne visités par l'utilisateur, et (c) en déterminant la valeur d'attribut démographique du document collecteur en ligne au moyen de l'estimation déterminée de la valeur d'attribut démographique pour chacune des utilisateurs de l'ensemble donné.
PCT/US2008/052515 2007-01-30 2008-01-30 Inférence probabiliste de démographie de site à partir d'utilisations utilisateur internet agrégées et information démographique source WO2008095031A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US11/699,745 2007-01-30
US11/699,745 US8290800B2 (en) 2007-01-30 2007-01-30 Probabilistic inference of site demographics from aggregate user internet usage and source demographic information

Publications (1)

Publication Number Publication Date
WO2008095031A1 true WO2008095031A1 (fr) 2008-08-07

Family

ID=39669012

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2008/052515 WO2008095031A1 (fr) 2007-01-30 2008-01-30 Inférence probabiliste de démographie de site à partir d'utilisations utilisateur internet agrégées et information démographique source

Country Status (2)

Country Link
US (2) US8290800B2 (fr)
WO (1) WO2008095031A1 (fr)

Cited By (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8892687B1 (en) 2013-12-06 2014-11-18 Shape Security, Inc. Client/server security by an intermediary rendering modified in-memory objects
US8893294B1 (en) 2014-01-21 2014-11-18 Shape Security, Inc. Flexible caching
US8954583B1 (en) 2014-01-20 2015-02-10 Shape Security, Inc. Intercepting and supervising calls to transformed operations and objects
US9027142B1 (en) 2014-01-21 2015-05-05 Shape Security, Inc. Dynamic field re-rendering
US9083739B1 (en) 2014-05-29 2015-07-14 Shape Security, Inc. Client/server authentication using dynamic credentials
US9210171B1 (en) 2014-05-29 2015-12-08 Shape Security, Inc. Selectively protecting valid links to pages of a web site
US9225737B2 (en) 2013-03-15 2015-12-29 Shape Security, Inc. Detecting the introduction of alien content
US9225729B1 (en) 2014-01-21 2015-12-29 Shape Security, Inc. Blind hash compression
US9258274B2 (en) 2014-07-09 2016-02-09 Shape Security, Inc. Using individualized APIs to block automated attacks on native apps and/or purposely exposed APIs
US9405910B2 (en) 2014-06-02 2016-08-02 Shape Security, Inc. Automatic library detection
US9411958B2 (en) 2014-05-23 2016-08-09 Shape Security, Inc. Polymorphic treatment of data entered at clients
US9438625B1 (en) 2014-09-09 2016-09-06 Shape Security, Inc. Mitigating scripted attacks using dynamic polymorphism
US9479526B1 (en) 2014-11-13 2016-10-25 Shape Security, Inc. Dynamic comparative analysis method and apparatus for detecting and preventing code injection and other network attacks
US9479529B2 (en) 2014-07-22 2016-10-25 Shape Security, Inc. Polymorphic security policy action
US9544329B2 (en) 2014-03-18 2017-01-10 Shape Security, Inc. Client/server security by an intermediary executing instructions received from a server and rendering client application instructions
US9608975B2 (en) 2015-03-30 2017-03-28 Shape Security, Inc. Challenge-dynamic credential pairs for client/server request validation
US9858440B1 (en) 2014-05-23 2018-01-02 Shape Security, Inc. Encoding of sensitive data
US9917850B2 (en) 2016-03-03 2018-03-13 Shape Security, Inc. Deterministic reproduction of client/server computer state or output sent to one or more client computers
US9954893B1 (en) 2014-09-23 2018-04-24 Shape Security, Inc. Techniques for combating man-in-the-browser attacks
US9986058B2 (en) 2015-05-21 2018-05-29 Shape Security, Inc. Security systems for mitigating attacks from a headless browser executing on a client computer
US10050935B2 (en) 2014-07-09 2018-08-14 Shape Security, Inc. Using individualized APIs to block automated attacks on native apps and/or purposely exposed APIs with forced user interaction
US10129289B1 (en) 2016-03-11 2018-11-13 Shape Security, Inc. Mitigating attacks on server computers by enforcing platform policies on client computers
US10187408B1 (en) 2014-04-17 2019-01-22 Shape Security, Inc. Detecting attacks against a server computer based on characterizing user interactions with the client computing device
US10212130B1 (en) 2015-11-16 2019-02-19 Shape Security, Inc. Browser extension firewall
US10230718B2 (en) 2015-07-07 2019-03-12 Shape Security, Inc. Split serving of computer code
US10298599B1 (en) 2014-09-19 2019-05-21 Shape Security, Inc. Systems for detecting a headless browser executing on a client computer
US10326790B2 (en) 2016-02-12 2019-06-18 Shape Security, Inc. Reverse proxy computer: deploying countermeasures in response to detecting an autonomous browser executing on a client computer
US10375026B2 (en) 2015-10-28 2019-08-06 Shape Security, Inc. Web transaction status tracking
US10567419B2 (en) 2015-07-06 2020-02-18 Shape Security, Inc. Asymmetrical challenges for web security
US10567363B1 (en) 2016-03-03 2020-02-18 Shape Security, Inc. Deterministic reproduction of system state using seeded pseudo-random number generators

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10839321B2 (en) 1997-01-06 2020-11-17 Jeffrey Eder Automated data storage system
US8321249B2 (en) * 2007-01-30 2012-11-27 Google Inc. Determining a demographic attribute value of an online document visited by users
US20090182615A1 (en) * 2008-01-14 2009-07-16 Microsoft Corporation Self-serve direct-to-consumer mail marketing service
CN103473721B (zh) 2010-12-20 2017-04-12 尼尔森（美国）有限公司 使用分布式人口统计信息确定媒体印象的方法和装置
AU2013204865B2 (en) 2012-06-11 2015-07-09 The Nielsen Company (Us), Llc Methods and apparatus to share online media impressions data
US10356485B2 (en) 2015-10-23 2019-07-16 The Nielsen Company (Us), Llc Methods and apparatus to calculate granular data of a region based on another region for media audience measurement
US9800928B2 (en) 2016-02-26 2017-10-24 The Nielsen Company (Us), Llc Methods and apparatus to utilize minimum cross entropy to calculate granular data of a region based on another region for media audience measurement

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20010013009A1 (en) * 1997-05-20 2001-08-09 Daniel R. Greening System and method for computer-based marketing
US20030055759A1 (en) * 2000-01-13 2003-03-20 Erinmedia, Inc. System and methods for creating and evaluating content and predicting responses to content
US20030101024A1 (en) * 2001-11-02 2003-05-29 Eytan Adar User profile classification by web usage analysis
EP1450284A2 (fr) * 1998-12-03 2004-08-25 Samsung Electronics Co., Ltd. Système de surveillance publicitaire

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1998043182A1 (fr) * 1997-03-24 1998-10-01 Queen's University At Kingston Procede, produits et dispositif pour detection de coincidences
US6327574B1 (en) * 1998-07-07 2001-12-04 Encirq Corporation Hierarchical models of consumer attributes for targeting content in a privacy-preserving manner
AU2001249080A1 (en) * 2000-02-29 2001-09-12 Expanse Networks, Inc. Privacy-protected targeting system
US20020152117A1 (en) * 2001-04-12 2002-10-17 Mike Cristofalo System and method for targeting object oriented audio and video content to users
US20040205157A1 (en) * 2002-01-31 2004-10-14 Eric Bibelnieks System, method, and computer program product for realtime profiling of web site visitors
US9235849B2 (en) 2003-12-31 2016-01-12 Google Inc. Generating user information for use in targeted advertising
US8229957B2 (en) * 2005-04-22 2012-07-24 Google, Inc. Categorizing objects, such as documents and/or clusters, with respect to a taxonomy and data structures derived from such categorization
AU2003243481A1 (en) * 2002-06-10 2003-12-22 Richard Van Horn Method and system for providing characterized on-line identities and matching credentials to individuals based on their profile
US8086559B2 (en) * 2002-09-24 2011-12-27 Google, Inc. Serving content-relevant advertisements with client-side device support
WO2007048229A1 (fr) * 2005-10-25 2007-05-03 Angoss Software Corporation Arborescences de stratégie pour une exploration de données
US20070180469A1 (en) * 2006-01-27 2007-08-02 William Derek Finley Method of demographically profiling a user of a computer system
US8321249B2 (en) 2007-01-30 2012-11-27 Google Inc. Determining a demographic attribute value of an online document visited by users

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20010013009A1 (en) * 1997-05-20 2001-08-09 Daniel R. Greening System and method for computer-based marketing
EP1450284A2 (fr) * 1998-12-03 2004-08-25 Samsung Electronics Co., Ltd. Système de surveillance publicitaire
US20030055759A1 (en) * 2000-01-13 2003-03-20 Erinmedia, Inc. System and methods for creating and evaluating content and predicting responses to content
US20030101024A1 (en) * 2001-11-02 2003-05-29 Eytan Adar User profile classification by web usage analysis

Cited By (49)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9973519B2 (en) 2013-03-15 2018-05-15 Shape Security, Inc. Protecting a server computer by detecting the identity of a browser on a client computer
US9609006B2 (en) 2013-03-15 2017-03-28 Shape Security, Inc. Detecting the introduction of alien content
US9225737B2 (en) 2013-03-15 2015-12-29 Shape Security, Inc. Detecting the introduction of alien content
US8892687B1 (en) 2013-12-06 2014-11-18 Shape Security, Inc. Client/server security by an intermediary rendering modified in-memory objects
US9270647B2 (en) 2013-12-06 2016-02-23 Shape Security, Inc. Client/server security by an intermediary rendering modified in-memory objects
US10027628B2 (en) 2013-12-06 2018-07-17 Shape Security, Inc. Client/server security by an intermediary rendering modified in-memory objects
US8954583B1 (en) 2014-01-20 2015-02-10 Shape Security, Inc. Intercepting and supervising calls to transformed operations and objects
US10212137B1 (en) 2014-01-21 2019-02-19 Shape Security, Inc. Blind hash compression
US9225729B1 (en) 2014-01-21 2015-12-29 Shape Security, Inc. Blind hash compression
US9027142B1 (en) 2014-01-21 2015-05-05 Shape Security, Inc. Dynamic field re-rendering
US8893294B1 (en) 2014-01-21 2014-11-18 Shape Security, Inc. Flexible caching
US9405851B1 (en) 2014-01-21 2016-08-02 Shape Security, Inc. Flexible caching
US10554777B1 (en) 2014-01-21 2020-02-04 Shape Security, Inc. Caching for re-coding techniques
US9584534B1 (en) 2014-01-21 2017-02-28 Shape Security, Inc. Dynamic field re-rendering
US9544329B2 (en) 2014-03-18 2017-01-10 Shape Security, Inc. Client/server security by an intermediary executing instructions received from a server and rendering client application instructions
US10187408B1 (en) 2014-04-17 2019-01-22 Shape Security, Inc. Detecting attacks against a server computer based on characterizing user interactions with the client computing device
US9411958B2 (en) 2014-05-23 2016-08-09 Shape Security, Inc. Polymorphic treatment of data entered at clients
US9858440B1 (en) 2014-05-23 2018-01-02 Shape Security, Inc. Encoding of sensitive data
US11552936B2 (en) 2014-05-29 2023-01-10 Shape Security, Inc. Management of dynamic credentials
US9621583B2 (en) 2014-05-29 2017-04-11 Shape Security, Inc. Selectively protecting valid links to pages of a web site
US9716702B2 (en) 2014-05-29 2017-07-25 Shape Security, Inc. Management of dynamic credentials
US9210171B1 (en) 2014-05-29 2015-12-08 Shape Security, Inc. Selectively protecting valid links to pages of a web site
US9083739B1 (en) 2014-05-29 2015-07-14 Shape Security, Inc. Client/server authentication using dynamic credentials
US9405910B2 (en) 2014-06-02 2016-08-02 Shape Security, Inc. Automatic library detection
US9258274B2 (en) 2014-07-09 2016-02-09 Shape Security, Inc. Using individualized APIs to block automated attacks on native apps and/or purposely exposed APIs
US10050935B2 (en) 2014-07-09 2018-08-14 Shape Security, Inc. Using individualized APIs to block automated attacks on native apps and/or purposely exposed APIs with forced user interaction
US9479529B2 (en) 2014-07-22 2016-10-25 Shape Security, Inc. Polymorphic security policy action
US9438625B1 (en) 2014-09-09 2016-09-06 Shape Security, Inc. Mitigating scripted attacks using dynamic polymorphism
US10868819B2 (en) 2014-09-19 2020-12-15 Shape Security, Inc. Systems for detecting a headless browser executing on a client computer
US10298599B1 (en) 2014-09-19 2019-05-21 Shape Security, Inc. Systems for detecting a headless browser executing on a client computer
US9954893B1 (en) 2014-09-23 2018-04-24 Shape Security, Inc. Techniques for combating man-in-the-browser attacks
US9479526B1 (en) 2014-11-13 2016-10-25 Shape Security, Inc. Dynamic comparative analysis method and apparatus for detecting and preventing code injection and other network attacks
US9608975B2 (en) 2015-03-30 2017-03-28 Shape Security, Inc. Challenge-dynamic credential pairs for client/server request validation
US10367903B2 (en) 2015-05-21 2019-07-30 Shape Security, Inc. Security systems for mitigating attacks from a headless browser executing on a client computer
US9986058B2 (en) 2015-05-21 2018-05-29 Shape Security, Inc. Security systems for mitigating attacks from a headless browser executing on a client computer
US10798202B2 (en) 2015-05-21 2020-10-06 Shape Security, Inc. Security systems for mitigating attacks from a headless browser executing on a client computer
US10567419B2 (en) 2015-07-06 2020-02-18 Shape Security, Inc. Asymmetrical challenges for web security
US10230718B2 (en) 2015-07-07 2019-03-12 Shape Security, Inc. Split serving of computer code
US10567386B2 (en) 2015-07-07 2020-02-18 Shape Security, Inc. Split serving of computer code
US10375026B2 (en) 2015-10-28 2019-08-06 Shape Security, Inc. Web transaction status tracking
US11171925B2 (en) 2015-10-28 2021-11-09 Shape Security, Inc. Evaluating and modifying countermeasures based on aggregate transaction status
US10212130B1 (en) 2015-11-16 2019-02-19 Shape Security, Inc. Browser extension firewall
US10826872B2 (en) 2015-11-16 2020-11-03 Shape Security, Inc. Security policy for browser extensions
US10326790B2 (en) 2016-02-12 2019-06-18 Shape Security, Inc. Reverse proxy computer: deploying countermeasures in response to detecting an autonomous browser executing on a client computer
US10567363B1 (en) 2016-03-03 2020-02-18 Shape Security, Inc. Deterministic reproduction of system state using seeded pseudo-random number generators
US10212173B2 (en) 2016-03-03 2019-02-19 Shape Security, Inc. Deterministic reproduction of client/server computer state or output sent to one or more client computers
US9917850B2 (en) 2016-03-03 2018-03-13 Shape Security, Inc. Deterministic reproduction of client/server computer state or output sent to one or more client computers
US10447726B2 (en) 2016-03-11 2019-10-15 Shape Security, Inc. Mitigating attacks on server computers by enforcing platform policies on client computers
US10129289B1 (en) 2016-03-11 2018-11-13 Shape Security, Inc. Mitigating attacks on server computers by enforcing platform policies on client computers

Also Published As

Publication number Publication date
US20130282428A1 (en) 2013-10-24
US20080183556A1 (en) 2008-07-31
US8290800B2 (en) 2012-10-16

Similar Documents

Publication Publication Date Title
US8290800B2 (en) Probabilistic inference of site demographics from aggregate user internet usage and source demographic information
US8676961B2 (en) System and method for web destination profiling
Parra-Arnau et al. Myadchoices: Bringing transparency and control to online advertising
Gomer et al. Network analysis of third party tracking: User exposure to tracking cookies through search
US8452832B2 (en) System and method for population-targeted advertising
Yang Web user behavioral profiling for user identification
Schafer et al. Collaborative filtering recommender systems
Kazienko et al. AdROSA—Adaptive personalization of web advertising
Castelluccia et al. Betrayed by your ads! Reconstructing user profiles from targeted ads
US8639575B2 (en) Audience segment estimation
US20080005313A1 (en) Using offline activity to enhance online searching
US20080004884A1 (en) Employment of offline behavior to display online content
US20150095145A1 (en) Advertisement effectiveness measurement
US20080126411A1 (en) Demographic prediction using a social link network
US20100318418A1 (en) Advertising inventory prediction for frequency-capped lines
US20110041168A1 (en) Systems and methods for targeting online advertisements using data derived from social networks
Dou et al. A privacy-preserving multimedia recommendation in the context of social network based on weighted noise injection
WO2008080104A1 (fr) Estimation de statistiques pour campagnes publicitaires en ligne
US20150242751A1 (en) System and method for estimating audience interest
US20120284119A1 (en) System and method for selecting web pages on which to place display advertisements
US20150235260A1 (en) Forecasting electronic events
US8321249B2 (en) Determining a demographic attribute value of an online document visited by users
He et al. Online learning for auction mechanism in bandit setting
Huang et al. Characterizing Web users' online information behavior
Boufoussi et al. On the local time of multifractional Brownian motion

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 08728600

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 08728600

Country of ref document: EP

Kind code of ref document: A1